Abstract

For the last six decades, engineering design has been advanced through the synergistic and principled use of theory, experiments and physics-based simulations. Our increased ability to sense, acquire and analyze data is clearly a game-changer. From data analytics and machine learning to digital twins, a powerful new set of engineering design tools are beginning to emerge. Yet, we must be careful not to overlook the limitations of data-driven approaches, especially since our engineering data — even if "big" —are almost always sparse, noisy and indirect. This talk will discuss the importance of bringing together the perspectives of data-driven learning and physics-based modeling, with a particular focus on methodologies for reduced-order modeling and predictive digital twins.

Biography

Karen E. Willcox is director of the Oden Institute for Computational Engineering and Sciences, associate vice president for research, and professor of aerospace engineering and engineering mechanics at The University of Texas at Austin. She is also external professor at the Santa Fe Institute. Before joining the Oden Institute in 2018, she spent 17 years as a professor at the Massachusetts Institute of Technology, where she served as the founding co-director of the MIT Center for Computational Engineering and the associate head of the MIT Department of Aeronautics and Astronautics. Prior to joining the MIT faculty, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group. She is a fellow of the Society for Industrial and Applied Mathematics, the American Institute of Aeronautics and Astronautics and the US Association for Computational Mechanics and a member of the National Academy of Engineering.

Hosted by the College of Engineering and the School of Mechanical Engineering.